Artificial Intelligence in Energy Catalysis Decision-Making

This document is a review article that discusses the application of artificial intelligence (AI) in energy catalysis, focusing on the transition from catalyst screening to informed decision-making. It outlines how machine learning is utilized to enhance the discovery of catalysts by addressing the limitations of traditional screening methods, which often do not account for the dynamic conditions under which catalysts operate. The review categorizes the advancements in AI into three significant shifts: from static descriptors to operando-relevant representations, from offline predictions to closed-loop discovery, and from single-objective optimization to sustainability-aware multi-objective optimization. Through case studies on CO2 electroreduction, acidic oxygen evolution, and higher alcohol synthesis, the authors illustrate how these shifts can influence catalyst ranking and experimental priorities. The review also emphasizes the importance of practical criteria for data reporting and validation in the context of AI applications in catalysis, aiming to improve the decision-making process in catalyst development.